ADWICE - Anomaly detection with real-time incremental clustering

Lecture Notes in Computer Science

Volumn 3506, Issue , 2005, Pages 407-424

  Burbeck, Kalle ^a   Nadjm Tehrani, Simin ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

Adaptability; Anomaly Detection; Clustering; Intrusion Detection; Real time

Indexed keywords

ALGORITHMS; COMPUTER ARCHITECTURE; DATA MINING; DATA REDUCTION; ONLINE SYSTEMS; REAL TIME SYSTEMS;

ADAPTABILITY; ANOMALY DETECTION; CLUSTERING; INTRUSION DETECTION; REAL-TIME;

ELECTRONIC DOCUMENT IDENTIFICATION SYSTEMS;

EID: 24944591774     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11496618_30     Document Type: Conference Paper

References (16)

1. Yegneswaran, V., Barford, P., Ullrich, J.: Internet intrusions: global characteristics and prevalence. In: Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, San Diego, CA, USA, ACM Press (2003) 138-147
2. McHugh, J.: Intrusion and intrusion detection. International Journal of Information Security 1 (2001) 14-35
3. Haines, J., Kewley Ryder, D., Tinnel, L., Taylor, S.: Validation of sensor alert correlators. IEEE Security and Privacy 1 (2003) 46-56
4. Chyssler, T., Nadjm-Tehrani, S., Burschka, S., Burbeck, K.: Alarm reduction and correlation in defence of ip networks. In: Proceedings of International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE04), Modena, Italy, IEEE Computer Society (2004) 229-234
5. Safeguard: The safeguard project (2003) http://www.ist-safeguard.org/ Acc. May 2004.
6. Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD Record 1996 ACM SIGMOD International Conference on Management of Data 25 (1996) 103-14
7. Fu, Y., Sandhu, K., Shih, M.Y.: A generalization-based approach to clustering of web usage sessions. In: Proceedings of the Web Usage Analysis and User Profiling. International WEBKDD'99 Workshop. Volume 1836 of Lecture Notes in Artificial Intelligence., San Diego, CA, USA, Springer-Verlag (2000) 21-38
8. Mukkamala, S., Janoski, G., Sung, A.: Intrusion detection using neural networks and support vector machines. In: Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN'02), Honolulu, HI, Institute of Electrical and Electronics Engineers Inc. (2002) 1702-1707
9. Elkan, C.: Results of the kdd'99 classifier learning. ACM SIGKDD Explorations 1 (2000) 63-64
10. Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: ACM Workshop on Data Mining Applied to Security. (2001)
11. Sequeira, K., Zaki, M.: Admit: Anomaly-based data mining for intrusions. In: Proceedings of the 8th ACM SIGKDD international conference on Knowledge discovery and data mining, Edmonton, Alberta, Canada, ACM Press (2002) 386-395
12. Guan, Y., Ghorbani, A.A., Belacel, N.: Y-means: A clustering method for intrusion detection. In: Canadian Conference on AI. Volume 2671 of Lecture Notes in Computer Science., Montreal, Canada, Springer (2003) 616-617
13. Munson, J., Wimer, S.: Watcher: the missing piece of the security puzzle. In: Proceedings of the 17th Annual Computer Security Applications Conference, New Orleans, LA, USA, IEEE Comput. Soc (2001) 230-9
14. Han, J., Kamber, M.: Data Mining - Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001)
15. Mahoney, M.V., Chan, P.K.: An analysis of the 1999 darpa/lincoln laboratory evaluation data for network anomaly detection. In: Recent Advances in Intrusion Detection. Volume 2820 of Lecture Notes in Computer Science., Pittsburgh, PA, USA, Springer (2003) 220-237
16. Axelsson, S.: The base-rate fallacy and the difficulty of intrusion detection. ACM Transactions on Information and Systems Security 3 (2000) 186-205